This paper examines the role of complex network patterns in determining international trade. The author proposes two empirical approaches to better identify the influences that the full structure of the trade network has on individual bilateral flows. The first uses gravity models that incorporate novel network covariates. The second uses exponential random graph models (ERGMs) that analyze trade from a network perspective. Estimates from both types of models provide strong evidence that network dependencies are influential determinants at both the intensive and extensive margin. Direct comparisons of the two approaches indicate that each can outperform the other at capturing and replicating certain types of network features. These results indicate that complex network patterns are an important determinant of trade, that gravity models can capture much of this dependency even if not explicitly controlled for, and that empirical network models such as ERGMs can be valuable tools for better capturing certain network patterns.

本文考察了复杂网络模式在决定国际贸易中的作用。作者提出了两种实证方法，以更好地确定贸易网络的完整结构对个别双边流动的影响。第一种使用包含新网络协变量的重力模型。第二种使用指数随机图模型 (ERGM)，从网络角度分析交易。来自这两种模型的估计提供了强有力的证据，表明网络依赖性在密集和广泛边缘都是有影响的决定因素。两种方法的直接比较表明，每种方法在捕获和复制某些类型的网络特征方面都可以胜过另一种方法。这些结果表明复杂的网络模式是贸易的重要决定因素，